CAREER: Learning, Estimation, and Control of Networked Epidemic Processes

职业：网络化流行病过程的学习、估计和控制

基本信息

批准号：
2238388
负责人：
Philip Pare
金额：
$ 51.35万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-02-01 至 2028-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238388&HistoricalAwards=false
关键词：
CAREER Learning Estimation Control Networked

项目摘要

Current approaches for controlling diseases spreading through populations employ techniques that rely solely on mathematical models or only depend on data, with no clear connection between these two extremes. The proposed research will establish a set of fundamental theories, tools, and algorithms to model, learn, and control real-time epidemic spreading processes by leveraging multiple live data streams while evaluating the trade-off between model-based and data-driven approaches. The proposed research consists of three thrusts. The first thrust will encompass the design of a set of novel models to characterize epidemic spreading under different settings, providing tools for connecting and comparing models at different resolutions. The next thrust will include algorithm design and development aimed at selecting the appropriate models and identification of model features by leveraging multiple live data streams. The last thrust will incorporate the multi-resolution models from the first two thrusts within a data-driven predictive control framework, studying model-based vs. data-driven approaches. This research will help provide insights for decision makers, such as politicians, public health officials, administrators, and business leaders, to better mitigate future disease outbreaks. The proposed research will develop a class of multi-resolution models that enable nonlinear control design that spans and adapts along the model-based vs. data-driven spectrum and is focused on the application of networked epidemic processes. The project will identify fundamental bounds on achievable performance in the presence of corrupted data sets and provide theories and algorithms with performance guarantees. The proposed research will lead to a greater understanding of the fundamental factors that affect the modeling, learning, and control of networked systems with multiple online data streams; establishing systematic procedures for model selection, estimating parameters and structure from uncertain and biased data; and developing realizable real-time control strategies for and across different model resolutions. The research plan will be integrated into education through a software platform to serve as an educational tool, giving students hands-on experience with data, exponential growth, dynamic modeling, and control.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

当前通过人群传播的疾病的当前方法采用仅依赖数学模型或仅取决于数据的技术，而这两个极端之间没有明确的联系。拟议的研究将建立一系列基本理论，工具和算法，以通过利用多个实时数据流来建模，学习和控制实时流行病扩散过程，同时评估基于模型和数据驱动的方法之间的权衡。拟议的研究包括三个推力。第一个推力将涵盖一组新型模型的设计，以表征在不同设置下流行病扩散，从而提供了连接和比较不同分辨率模型的工具。下一个推力将包括算法设计和开发，旨在通过利用多个实时数据流来选择适当的模型和识别模型功能。最后一个推力将在数据驱动的预测控制框架中结合前两个推力的多分辨率模型，从而研究基于模型的模型与数据驱动的方法。这项研究将有助于为决策者提供见解，例如政客，公共卫生官员，管理人员和商业领袖，以更好地减轻未来的疾病爆发。拟议的研究将开发一类多分辨率模型，该模型可以启用非线性控制设计，该设计跨越基于模型和数据驱动的频谱，并专注于网络流行过程的应用。该项目将在损坏的数据集存在下确定可实现性能的基本界限，并提供具有性能保证的理论和算法。拟议的研究将使对影响具有多个在线数据流的网络系统的建模，学习和控制的基本因素有更深入的了解；建立用于模型选择的系统程序，从不确定和有偏见的数据中估算参数和结构；并在不同模型分辨率上制定可实现的实时控制策略。该研究计划将通过软件平台整合到教育中，以作为一种教育工具，为学生提供数据，指数增长，动态建模和控制的实践经验。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来进行评估的。